Hybrid AI Models for Smart Stormwater Infrastructure Management

Abstract

Urban stormwater systems are increasingly challenged by climate variability, rapid urbanization, and complex pollutant dynamics. Traditional monitoring and management approaches are often insufficient for handling real-time variability in flow and water quality. Hybrid Artificial Intelligence (AI) models, which integrate multiple machine learning and deep learning techniques, offer a promising solution for smart stormwater infrastructure management. This study explores hybrid AI frameworks combining methods such as LSTM, Random Forest (RF), Gradient Boosting (GB), and optimization algorithms to enhance prediction accuracy for stormwater flow, pollutant concentration, and system performance. Model evaluation using R², RMSE, and MAE demonstrates that hybrid models outperform standalone algorithms by effectively capturing both nonlinear relationships and temporal dependencies. The integration of IoT-based real-time monitoring further improves predictive capability and enables adaptive infrastructure control. The findings highlight hybrid AI systems as efficient, scalable, and intelligent solutions for next-generation stormwater management.